Instructions to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("JANGQ-AI/LFM2.5-8B-A1B-JANG_2L") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default JANGQ-AI/LFM2.5-8B-A1B-JANG_2L
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JANGQ-AI/LFM2.5-8B-A1B-JANG_2L with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "JANGQ-AI/LFM2.5-8B-A1B-JANG_2L" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download jang_config.json from JANGQ-AI/LFM2.5-8B-A1B-JANG_2L: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/JANGQ-AI/LFM2.5-8B-A1B-JANG_2L/resolve/main/jang_config.json
- Command line
-
hf download hf://JANGQ-AI/LFM2.5-8B-A1B-JANG_2L/jang_config.json
-
curl -L -o jang_config.json https://huggingface.co/JANGQ-AI/LFM2.5-8B-A1B-JANG_2L/resolve/main/jang_config.json
1.17 kB
| { | |
| "quantization": { | |
| "method": "jang-importance", | |
| "profile": "JANG_2L", | |
| "target_bits": 2.0, | |
| "actual_bits": 2.37, | |
| "block_size": 64, | |
| "calibration_method": "weights", | |
| "quantization_method": "mse", | |
| "scoring_method": "weight-magnitude", | |
| "bit_widths_used": [ | |
| 2, | |
| 6, | |
| 8 | |
| ], | |
| "passthrough_bit_widths_used": [ | |
| 16 | |
| ], | |
| "passthrough_tensor_count": 18, | |
| "quantization_scheme": "asymmetric", | |
| "quantization_backend": "mx.quantize", | |
| "hadamard_rotation": false | |
| }, | |
| "source_model": { | |
| "name": "LFM2.5-8B-A1B", | |
| "dtype": "bfloat16", | |
| "parameters": "34.5B" | |
| }, | |
| "architecture": { | |
| "type": "hybrid_moe_ssm", | |
| "attention": "gqa", | |
| "has_vision": false, | |
| "has_ssm": true, | |
| "has_moe": true | |
| }, | |
| "runtime": { | |
| "total_weight_bytes": 3044371880, | |
| "total_weight_gb": 2.84 | |
| }, | |
| "capabilities": { | |
| "reasoning_parser": "qwen3", | |
| "tool_parser": "lfm2", | |
| "think_in_template": false, | |
| "supports_tools": true, | |
| "supports_thinking": true, | |
| "family": "lfm2_moe", | |
| "modality": "text", | |
| "cache_type": "hybrid" | |
| }, | |
| "format": "jang", | |
| "format_version": "2.0" | |
| } | |